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Remote Sensing of the Water Cycle

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (21 April 2021) | Viewed by 26962

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Prof. Dr. Vincenzo Levizzani

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Consiglio Nazionale delle Ricerche, Istituto di Scienze dell’Atmosfera e del Clima, via Gobetti 101, 40129 Bologna, Italy
Interests: clouds and precipitation structure; passive and active remote sensing of precipitation from satellite; climatology of precipitation
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Special Issue Information

Dear Colleagues,

Remote sensing methods, techniques, and models are essential tools for a thorough understanding of Water Cycle mechanisms. The Section “Remote Sensing of the Water Cycle” of the journal Remote Sensing aims at providing a forum for research in this field contributing to advancing science and transferring results and concepts to operational applications.

The water cycle is very complicated in nature since a number of components are involved, such as terrestrial ecosystems both in the plains and mountain regions, rivers, lakes, wetlands, dry areas, forests, polar ice caps, oceans, and glaciers. Mechanisms involve soil–vegetation–atmosphere exchanges through percolation, plant uptake, evapotranspiration, infiltration, runoff, and storage and flow of groundwater. Exchanges of energy, evaporation, and evapotranspiration move enormous quantities of sensible and latent heat and water during the cycle. All this and much more is currently not adequately addressed by observing systems and, consequently, Earth modeling. Research needs to be focused on knowledge gaps also to help to channel existing resources and attract new ones.

Given the global as well as regional character of water cycle processes, remote sensing helps considerably in monitoring the mechanisms of the water cycle and their sudden or long-term changes. Datasets are starting to have the necessary length to explore changes at the monthly, annual, seasonal, interannual, and decadal scales. Long-term monitoring requires that such datasets be maintained and continuously corrected for sensor changes and observation/processing errors.

The Special Issue aims at collecting a series of high-profile Feature Papers that should set the scene for the Section and share actual knowledge of the cycle from a remote sensing perspective. Papers are solicited on (but not limited to) the following topics:

New satellite missions;
New observing systems;
Synergy between satellite and ground based remote sensing sensors;
Observation of insufficiently quantified components of the water cycle;
Water cycle of climate hot spots;
Advances in retrieval algorithms;
Use of remote sensing data in water cycle and Earth system models;
The water cycle and its implications for an efficient water management.

Prof. Dr. Vincenzo Levizzani

Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Water cycle
Precipitation
Clouds
Water vapor
Evaporation
Evapotranspiration
Soil moisture
Water storage
Satellite missions
Climate
Ecosystems

Published Papers (7 papers)

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Research

50 pages, 19957 KiB

Open AccessArticle

Comparison of Historical Water Temperature Measurements with Landsat Analysis Ready Data Provisional Surface Temperature Estimates for the Yukon River in Alaska

by Carson A. Baughman and Jeffrey S. Conaway

Remote Sens. 2021, 13(12), 2394; https://doi.org/10.3390/rs13122394 - 19 Jun 2021

Cited by 3 | Viewed by 2810

Abstract

Water temperature is a key element of freshwater ecological systems and a critical element within natural resource monitoring programs. In the absence of in situ measurements, remote sensing platforms can indirectly measure water temperature over time and space. The Earth Resources Observation and Science (EROS) Center has processed archived Landsat imagery into analysis ready data (ARD), including Level-2 Provisional Surface Temperature (pST) estimates derived from the Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Thermal Infrared Sensor (TIRS). We compared in situ measurements of water temperature within the Yukon River in Alaska with 52 instances of pST estimates between June 2014 and September 2020. Agreement was good with an RMSE of 2.25 °C and only a slight negative bias in the estimated mean daily water temperature of −0.47 °C. For the 52 dates compared, the average daily water temperature measured by the USGS streamgage was 11.3 °C with a standard deviation of 5.7 °C. The average daily pST estimate was 10.8 °C with a standard deviation of 6.1 °C. At least in the case of large unstratified rivers in Alaska, ARD pST can be used to infer water temperature in the absence of or in tandem with ground-based water temperature monitoring campaigns. Full article

(This article belongs to the Special Issue Remote Sensing of the Water Cycle)

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23 pages, 5815 KiB

Open AccessArticle

Retrieval of Daytime Total Column Water Vapour from OLCI Measurements over Land Surfaces

by René Preusker, Cintia Carbajal Henken and Jürgen Fischer

Remote Sens. 2021, 13(5), 932; https://doi.org/10.3390/rs13050932 - 2 Mar 2021

Cited by 13 | Viewed by 2938

Abstract

A new retrieval of total column water vapour (TCWV) from daytime measurements over land of the Ocean and Land Colour Instrument (OLCI) on-board the Copernicus Sentinel-3 missions is presented. The Copernicus Sentinel-3 OLCI Water Vapour product (COWa) retrieval algorithm is based on the differential absorption technique, relating TCWV to the radiance ratio of non-absorbing band and nearby water vapour absorbing band and was previously also successfully applied to other passive imagers Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS). One of the main advantages of the OLCI instrument regarding improved TCWV retrievals lies in the use of more than one absorbing band. Furthermore, the COWa retrieval algorithm is based on the full Optimal Estimation (OE) method, providing pixel-based uncertainty estimates, and transferable to other Near-Infrared (NIR) based TCWV observations. Three independent global TCWV data sets, i.e., Aerosol Robotic Network (AERONET), Atmospheric Radiation Measurement (ARM) and U.S. SuomiNet, and a German Global Navigation Satellite System (GNSS) TCWV data set, all obtained from ground-based observations, serve as reference data sets for the validation. Comparisons show an overall good agreement, with absolute biases between 0.07 and 1.31 kg/m² and root mean square errors (RMSE) between 1.35 and 3.26 kg/m². This is a clear improvement in comparison to the operational OLCI TCWV Level 2 product, for which the bias and RMSEs range between 1.10 and 2.55 kg/m² and 2.08 and 3.70 kg/m², respectively. A first evaluation of pixel-based uncertainties indicates good estimated uncertainties for lower retrieval errors, while the uncertainties seem to be overestimated for higher retrieval errors. Full article

(This article belongs to the Special Issue Remote Sensing of the Water Cycle)

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18 pages, 7326 KiB

Open AccessArticle

Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

by Jie Hsu, Wan-Ru Huang, Pin-Yi Liu and Xiuzhen Li

Remote Sens. 2021, 13(2), 254; https://doi.org/10.3390/rs13020254 - 13 Jan 2021

Cited by 32 | Viewed by 3811

Abstract

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database. Full article

(This article belongs to the Special Issue Remote Sensing of the Water Cycle)

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24 pages, 9866 KiB

Open AccessArticle

Assessment of IMERG-V06, TRMM-3B42V7, SM2RAIN-ASCAT, and PERSIANN-CDR Precipitation Products over the Hindu Kush Mountains of Pakistan, South Asia

by Ali Hamza, Muhammad Naveed Anjum, Muhammad Jehanzeb Masud Cheema, Xi Chen, Arslan Afzal, Muhammad Azam, Muhammad Kamran Shafi and Aminjon Gulakhmadov

Remote Sens. 2020, 12(23), 3871; https://doi.org/10.3390/rs12233871 - 26 Nov 2020

Cited by 31 | Viewed by 3127

Abstract

In this study, the performances of four satellite-based precipitation products (IMERG-V06 Final-Run, TRMM-3B42V7, SM2Rain-ASCAT, and PERSIANN-CDR) were assessed with reference to the measurements of in-situ gauges at daily, monthly, seasonal, and annual scales from 2010 to 2017, over the Hindu Kush Mountains of Pakistan. The products were evaluated over the entire domain and at point-to-pixel scales. Different evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias)) and categorical indices (False Alarm Ration (FAR), Critical Success Index (CSI), Success Ratio (SR), and Probability of Detection (POD)) were used to assess the performances of the products considered in this study. Our results indicated the following. (1) IMERG-V06 and PERSIANN capably tracked the spatio-temporal variation of precipitation over the studied region. (2) All satellite-based products were in better agreement with the reference data on the monthly scales than on daily time scales. (3) On seasonal scale, the precipitation detection skills of IMERG-V06 and PERSIANN-CDR were better than those of SM2Rain-ASCAT and TRMM-3B42V7. In all seasons, overall performance of IMERG-V06 and PERSIANN-CDR was better than TRMM-3B42V7 and SM2Rain-ASCAT. (4) However, all products were uncertain in detecting light and moderate precipitation events. Consequently, we recommend the use of IMERG-V06 and PERSIANN-CDR products for subsequent hydro-meteorological studies in the Hindu Kush range. Full article

(This article belongs to the Special Issue Remote Sensing of the Water Cycle)

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25 pages, 18156 KiB

Open AccessArticle

Meteorological OSSEs for New Zenith Total Delay Observations: Impact Assessment for the Hydroterra Geosynchronous Satellite on the October 2019 Genoa Event

by Martina Lagasio, Agostino N. Meroni, Giorgio Boni, Luca Pulvirenti, Andrea Monti-Guarnieri, Roger Haagmans, Stephen Hobbs and Antonio Parodi

Remote Sens. 2020, 12(22), 3787; https://doi.org/10.3390/rs12223787 - 18 Nov 2020

Cited by 6 | Viewed by 2305

Abstract

Along the Mediterranean coastlines, intense and localized rainfall events are responsible for numerous casualties and several million euros of damage every year. Numerical forecasts of such events are rarely skillful, because they lack information in their initial and boundary conditions at the relevant spatio-temporal scales, namely O(km) and O(h). In this context, the tropospheric delay observations (strongly related to the vertically integrated water vapor content) of the future geosynchronous Hydroterra satellite could provide valuable information at a high spatio-temporal resolution. In this work, Observing System Simulation Experiments (OSSEs) are performed to assess the impact of assimilating this new observation in a cloud-resolving meteorological model, at different grid spacing and temporal frequencies, and with respect to other existent observations. It is found that assimilating the Hydroterra observations at 2.5 km spacing every 3 or 6 h has the largest positive impact on the forecast of the event under study. In particular, a better spatial localization and extent of the heavy rainfall area is achieved and a realistic surface wind structure, which is a crucial element in the forecast of such heavy rainfall events, is modeled. Full article

(This article belongs to the Special Issue Remote Sensing of the Water Cycle)

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26 pages, 6510 KiB

Open AccessArticle

A New Method for Hail Detection from the GPM Constellation: A Prospect for a Global Hailstorm Climatology

by Sante Laviola, Giulio Monte, Vincenzo Levizzani, Ralph R. Ferraro and James Beauchamp

Remote Sens. 2020, 12(21), 3553; https://doi.org/10.3390/rs12213553 - 30 Oct 2020

Cited by 13 | Viewed by 5045

Abstract

A new method for detecting hailstorms by using all the MHS-like (MHS, Microwave Humidity Sounder) satellite radiometers currently in orbit is presented. A probability-based model originally designed for AMSU-B/MHS-based (AMSU-B, Advanced Microwave Sounding Unit-B) radiometers has been fitted to the observations of all microwave radiometers onboard the satellites of the Global Precipitation Measurements (GPM) constellation. All MHS-like frequency channels in the 150–170 GHz frequency range were adjusted on the MHS channel 2 (157 GHz) in order to account for the instrumental differences and tune the original model on the MHS-like technical characteristics. The novelty of this approach offers the potential of retrieving a uniform and homogeneous hail dataset on the global scale. The application of the hail detection model to the entire GPM constellation demonstrates the high potential of this generalized model to map the evolution of hail-bearing systems at very high temporal rate. The results on the global scale also demonstrate the high performances of the hail model in detecting the differences of hailstorm structure across the two hemispheres by means of a thorough reconstruction of the seasonality of the events particularly in South America where the largest hailstones are typically observed. Full article

(This article belongs to the Special Issue Remote Sensing of the Water Cycle)

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16 pages, 16602 KiB

Open AccessArticle

An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

by Bilawal Abbasi, Zhihao Qin, Wenhui Du, Jinlong Fan, Chunliang Zhao, Qiuyan Hang, Shuhe Zhao and Shifeng Li

Remote Sens. 2020, 12(21), 3469; https://doi.org/10.3390/rs12213469 - 22 Oct 2020

Cited by 17 | Viewed by 4741

Abstract

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO₂, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm² for the ARMP and 0.26 g/cm² for the AERONET datasets, with bias of 0.072 g/cm² and 0.096 g/cm² for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales. Full article

(This article belongs to the Special Issue Remote Sensing of the Water Cycle)

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